{"type":"ScholarlyArticle","title":"Distributed Clustering Based on Sampling Local Density Estimates","authors":[{"name":"Matthias Klusch","slug":"matthias-klusch","given":"Matthias","family":"Klusch","affiliation":""},{"name":"Stefano Lodi","slug":"stefano-lodi","given":"Stefano","family":"Lodi","affiliation":""},{"name":"Gianluca Moro","slug":"gianluca-moro","given":"Gianluca","family":"Moro","affiliation":""}],"abstract":"Huge amounts of data are stored in autonomous, geographically distributed sources. The discovery of previously unknown, implicit and valuable knowledge is a key aspect of the exploitation of such sources. In recent years several approaches to knowledge discovery and data mining, and in particular to clustering, have been developed, but only a few of them are designed for distributed data sources. We propose a novel distributed clustering algorithm based on non-parametric kernel density estimation, which takes into account the issues of privacy and communication costs that arise in a distributed environment.","doi":null,"url":"https://ijcai.org/proceedings/2003/72","pdf":"https://ijcai.org/proceedings/2003/0072.pdf","markdown":"https://ijcai.org/proceedings/2003/72.md","bibtex":"https://ijcai.org/proceedings/2003/bibtex/72","volume":{"slug":"2003","title":"Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2003","url":"https://ijcai.org/proceedings/2003/","year":2003,"location":"Acapulco, Mexico","dates":"","editors":"Georg Gottlob","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":72,"pages":{"first":485,"last":489},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}